Non-invasive Assessment of Pancreatic Duct Hypertension Using Computational Flow Modeling

TL;DR

This study introduces a non-invasive method combining MRCP imaging and computational modeling to estimate pancreatic duct pressure, aiding in diagnosis and treatment planning for chronic pancreatitis-related duct hypertension.

Contribution

The paper presents a novel non-invasive approach using patient-specific imaging and CFD modeling to estimate pancreatic duct pressure, validated against invasive measurements.

Findings

Strong correlation between simulated and in vivo pressure measurements

The analytical model accurately predicts PDP from ductal geometry

Potential for non-invasive diagnosis of pancreatic duct hypertension

Abstract

Chronic pancreatitis (CP) is a progressive inflammatory disease frequently associated with severe, treatment-resistant abdominal pain, which is hypothesized to result from pancreatic ductal hypertension (PDH) secondary to ductal strictures or obstructions. However, direct measurement of pancreatic duct pressure (PDP) remains technically demanding and invasive, thereby significantly limiting its routine clinical application. Here, we propose and validate a novel, non-invasive approach for estimating PDP. The method integrates patient-specific magnetic resonance cholangiopancreatography (MRCP) imaging with computational fluid dynamics (CFD) modeling. Three-dimensional ductal models reconstructed from MRCP data enabled simulation of intraductal pressure distributions with high anatomical fidelity. The simulated pressure gradients showed strong correlation with in vivo measurements obtained via endoscopic retrograde cholangiopancreatography (ERCP), as well as with clinical outcomes such as pain relief following ductal decompression. To improve clinical usability, we developed a quasi-one-dimensional analytical model that accurately predicted PDP from ductal geometry and flow parameters, showing strong concordance with CFD results. These findings establish the feasibility and clinical relevance of MRCP-based PDP estimation, and underscore its potential as a non-invasive diagnostic tool for detecting PDH and informing therapeutic decisions in patients with CP.